Summary of What If…?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models, by Junho Kim et al.
What if…?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models
by Junho Kim, Yeon Ju Kim, Yong Man Ro
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method called Counterfactual Inception for enhancing the reliability of Large Multi-modal Models (LMMs) in addressing hallucination. The method implants counterfactual thinking into LMMs using self-generated counterfactual keywords, enabling the models to engage with and generate responses that span a wider contextual scene understanding. To achieve this, the paper introduces Plausibility Verification Process (PVP), a simple yet robust keyword constraint that filters out sub-optimal keywords and enables consistent triggering of counterfactual thinking in model responses. The authors demonstrate the effectiveness of their approach through comprehensive analyses across various LMMs, including both open-source and proprietary models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes large language models better by teaching them to think about what could have happened differently. This helps the models understand the context more broadly and reduces the chance of making mistakes. The authors developed a new way called Counterfactual Inception that uses special keywords to make the models think like humans do when they consider alternative realities. They also created a simple test to check if the model’s response makes sense, which helps get rid of bad responses. By testing their approach on many different language models, the authors showed that it works and can improve the accuracy of these powerful tools. |
Keywords
» Artificial intelligence » Hallucination » Multi modal » Scene understanding
